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1 Alfonso Ortega Villanova University Site Director Associate Vice President Research and Graduate Programs James R. Birle Professor of Energy Technology Villanova University

2 Villanova University Personnel Site Director: Dr. Alfonso Ortega Faculty team: Dr. Amy Fleischer, Professor, Thermal Systems Dr. Aaron Wemhoff, Asst. Prof, Thermal Systems Dr. Jerry Jones, Professor, Thermal Modeling Dr. Kamran Fouladi, Research Prof, CFD Students: Luis Silva, Post-Doctoral Research Associate Khosrow Ebrahimi, Post-Doctoral Research Associate Marcelo Del Valle, PhD Candidate Anish Bhalerao, PhD Candidate Roozbeh Bahkshi, PhD Candidate Daniel Fritch, M.S. Candidate

3 Villanova University Research Focus Areas Local vs. Grid Renewables CHP DC vs. AC Energy Smart IT Smart Scheduling Holistic IT and Cooling Processes VTAS Energy Simulator Exergy Based methods for Analysis of Energy Efficient Dynamic Close-Coupled Cooling Liquid Cooling Energy Recovery Liquid Cooling to Waste Heat Recovery Organic Rankine Cycles Absorption Refrigeration Power Generation and Distribution Energy Smart Cooling and Energy Management

4 Villanova University Lead Site DATA CENTER SYSTEM ENERGY MODELING-CHIP TO COOLING TOWER

5 A flow network tool for modeling Data Center energy utilization Chip to Cooling tower Villanova Thermodynamic Analysis of Systems (VTAS): flow network modeling tool - 1 st Law (PUE, WUE) and 2 nd Law (exergy destruction) component and system efficiency calculations - Component design requirement calculations based on data center load - Modeling of transient behavior (e.g., CRAH and/or server failure) - System optimization - Comparison of nontraditional cooling schemes (rear-door heat exchanger, in-row heat exchanger) Servers & Racks VTAS connects component models via a flow framework Return plenum CRAH Chiller Rack Cold aisle Hot aisle CRAH Supply plenum Room Air Physical Layout Cooling tower Tile Cooling tower Computational Layout Chiller

6 Data Center Cooling System Component Models A large component model library has been created - Internal component models: - Servers - Data Center airspace (hot aisles, cold aisles, return plenum, supply plenum, vent tiles) - Rear-door heat exchangers - In-row coolers - External component models: - CRAH units - Chillers - Cooling towers - Other component models: - Fans/pumps - Flow junctions/flow splitters Component models calculate heat exchange, mass exchange, and exergy destruction Counterflow cooling tower component model with mass exchange Heat Exchange Exergy Destruction

7 Using Computational Fluid Dynamics as a tool to identify system inefficiencies Cooling systems cost are directly related to system inefficiencies Inefficiencies are directly related to system irreversibilities Irreversibilities can be identified using EXERGY analysis We are perfecting the ability to compute EXERGY DESTRUCTION within a CFD environment We are EXPORTING that information to the Energy Simulation tool (VTAS) using a POD technique perfected by our Georgia Tech partners (Joshi et al.) 7

8 Contributions of laminar and turbulent flow components to Exergy destruction Average: Fluctuating: 8

9 Binghamton University, Villanova University, University Texas-Arlington DYNAMIC AND HYBRID COOLING SYSTEMS

10 Dynamic Local On-Demand Cooling Dynamic data center IT and cooling operation Source: IT characteristics Dynamic IT load Each rack has different IT loads (Power dissipation) Legacy air cooling systems Cooling does not follow the IT load Cooling is not localized Servers are nearly always OVERPROVISIONED Dynamic On-Demand Cooling systems Synergistic control of cooling with load Distributed Close Coupled Hybrid cooling systems Cooling provided WHERE it is needed WHEN it is needed Temperature distribution in a traditional data center Source: 10

11 Hybrid Cooling Systems Hybrid cooling systems such as in-row, rear-door and over-head provide localized and dynamic cooling They are a tradeoff between liquid cooling and air cooling Air is used to cool the CPUs keeping liquid outside of the racks a) b) Heat is extracted as close as possible to the server using an air-liquid, cross-flow heat exchanger The proximity of the heat exchanger to the heat source increases the opportunities to recover energy Hybrid air liquid cooling systems; a) In-row cooling system, b) Rear door heat exchanger, c) Over head cooling system c) 11

12 Ongoing Research Active Control of DC Room Airflow Crossflow Air Liquid Heat Exchangers Complete and compact models to describe dynamic behavior Experimental validation Racks and servers The transient behavior of servers and racks has been modeled using numerical simulations and lab experiments Complex room airflow and associated irreversibilities CFD and analytical models used to develop performance metrics and compact models Over head cooling system representation 12

13 Modeling of HTX: Impact of heat capacity ratio Non-uniform hot fluid inlet temperature Temperature results Time constant Heat capacity rate ratio effect Hot fluid Final value Settling time Uniform Uniform Nonuniform Nonuniform E= E= Results-for smaller E, there is a smaller difference between the results for the non-uniform and uniform temperature boundary condition. 13

14 Additional non-uniform inlet temperature Models Model 1: Cold fluid non-uniform temperature boundary condition Model 2: Hot and Cold fluid non-uniform temperature boundary conditions Non-uniform temperature boundary conditions are imposed by making the temperature vary linearly across the inlet All the non-uniform temperature boundary conditions have same mean inlet temperature The inlet temperature boundary conditions are both non-uniform; in addition, the hot fluid inlet temperature is a function of time as well Both hot and cold non-uniform temperature boundary conditions are specified as a linear function of location 14

15 Dynamic HTX Measurements Air Heater Section Flow Obstruction Gate Heat Exchanger under Test

16 Dynamic variation of inlet water temperature Accomplished by controlled mixing of hot and cold fluid streams 16

17 Villanova University, Lead Site WASTE ENERGY RECOVERY

18 Data Center Waste Heat Recovery Concepts: Mapped by type of cooling # 1: No # 2: Yes # 3: Yes Desalination # 1 :Yes, with booster # 2: Yes # 3: Yes Organic Rankine Cycle Selected by ES2 Industrial mentors for further investigation Absorption Refrigeration # 1 :Yes # 2: Yes # 3: Yes # 1: Yes # 2: Yes # 3: Yes Biomass processing HVAC/ Domestic hot water # 1 :Yes # 2: Yes # 3: Yes Cooling Type Waste heat quality- Max ( o C) #1 (Air-cooled) # 1: No # 2: No # 3: Yes Thermoelectrics #2 ( Water-cooled) #3 (Two-phase cooled) District Heating # 1 :Yes, with booster # 2: Yes # 3: Yes # 1: Yes # 2: No # 3: No Piezoelectrics Boiler feed water heating # 1: No # 2: Yes # 3: Yes

19 Integration of Absorption Refrigeration and ORC to the server/rack cooling cycle The novel configuration to recover the waste heat and convert it to useful cooling- The condenser in on chip cooling cycle is replaced by generator of AR Simplification of the on-chip cooling cycle and integration into on-chip cooling cycle The on-chip two-phase cooling cycle developed by Thome research team at EPFL The novel configuration to recover the waste heat and convert it to electricity- The condenser in on chip cooling cycle is replaced by evaporator of ORC

20 Facilities The Laboratory for Advanced Thermal and Fluid Systems at Villanova University (Ortega) NovaTherm: Villanova s Thermal Management Laboratory (Fleischer) Multiscale System Analysis Laboratory (Wemhoff) Thermal and Flow Management of Multiscale Systems (Jones)

21 The Villanova-Steel ORCA Research Center (VSORC) 2500 sq. ft. Air Cooling Laboratory 3 ft. Raised Floor Labs A & B can be isolated Designed to allow remote launching of experiments and remote measurements 1000 sq. ft. Advanced Technology Laboratory Advanced Technology Lab designed for liquid cooling with maximum flexibility 3/17/2014 ES2 BRIEFING 21

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23 Additional University Support Villanova NSF ES2 is part of the Villanova Center for the Advancement of Sustainability in Engineering Our students receive University Tuition Fellowships Research Experiences for Undergraduate Students (REU) Research Experiences for Teachers (RET)